Overview

Dataset statistics

Number of variables20
Number of observations1134
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory177.3 KiB
Average record size in memory160.1 B

Variable types

Numeric10
Categorical6
Unsupported4

Alerts

pickup_place has a high cardinality: 189 distinct values High cardinality
item_name has a high cardinality: 774 distinct values High cardinality
item_category_name has a high cardinality: 321 distinct values High cardinality
how_long_it_took_to_order has a high cardinality: 862 distinct values High cardinality
pickup_lat is highly correlated with pickup_lon and 1 other fieldsHigh correlation
pickup_lon is highly correlated with pickup_lat and 1 other fieldsHigh correlation
dropoff_lat is highly correlated with pickup_lat and 1 other fieldsHigh correlation
dropoff_lon is highly correlated with pickup_lon and 1 other fieldsHigh correlation
pickup_lat is highly correlated with pickup_lon and 1 other fieldsHigh correlation
pickup_lon is highly correlated with pickup_lat and 1 other fieldsHigh correlation
dropoff_lat is highly correlated with pickup_lat and 1 other fieldsHigh correlation
dropoff_lon is highly correlated with pickup_lon and 1 other fieldsHigh correlation
pickup_lat is highly correlated with dropoff_latHigh correlation
dropoff_lat is highly correlated with pickup_latHigh correlation
place_category is highly correlated with pickup_lat and 3 other fieldsHigh correlation
pickup_lat is highly correlated with place_category and 3 other fieldsHigh correlation
pickup_lon is highly correlated with place_category and 2 other fieldsHigh correlation
dropoff_lat is highly correlated with place_category and 3 other fieldsHigh correlation
dropoff_lon is highly correlated with place_category and 3 other fieldsHigh correlation
distance_crossed is highly correlated with dropoff_latHigh correlation
how_long_it_took_to_order is uniformly distributed Uniform
df_index has unique values Unique
when_the_delivery_started is an unsupported type, check if it needs cleaning or further analysis Unsupported
when_the_courier_arrived_at_pickup is an unsupported type, check if it needs cleaning or further analysis Unsupported
when_the_courier_left_pickup is an unsupported type, check if it needs cleaning or further analysis Unsupported
when_the_courier_arrived_at_dropoff is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-01-30 01:27:33.929704
Analysis finished2022-01-30 01:27:52.933505
Duration19 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1134
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3007.124339
Minimum4
Maximum5982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:53.061043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile271.95
Q11516.5
median2979
Q34555.25
95-th percentile5733.75
Maximum5982
Range5978
Interquartile range (IQR)3038.75

Descriptive statistics

Standard deviation1737.737003
Coefficient of variation (CV)0.5778733457
Kurtosis-1.199333741
Mean3007.124339
Median Absolute Deviation (MAD)1521.5
Skewness-0.006510232407
Sum3410079
Variance3019729.89
MonotonicityStrictly increasing
2022-01-30T02:27:53.190066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41
 
0.1%
40031
 
0.1%
40281
 
0.1%
40241
 
0.1%
40211
 
0.1%
40191
 
0.1%
40171
 
0.1%
40071
 
0.1%
39931
 
0.1%
40361
 
0.1%
Other values (1124)1124
99.1%
ValueCountFrequency (%)
41
0.1%
261
0.1%
271
0.1%
291
0.1%
321
0.1%
351
0.1%
461
0.1%
501
0.1%
571
0.1%
671
0.1%
ValueCountFrequency (%)
59821
0.1%
59751
0.1%
59731
0.1%
59581
0.1%
59571
0.1%
59551
0.1%
59521
0.1%
59501
0.1%
59491
0.1%
59481
0.1%

delivery_id
Real number (ℝ≥0)

Distinct918
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1383667.07
Minimum1272737
Maximum1491424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:53.360448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1272737
5-th percentile1286826
Q11325357.25
median1381432
Q31440464.5
95-th percentile1480958
Maximum1491424
Range218687
Interquartile range (IQR)115107.25

Descriptive statistics

Standard deviation63921.23703
Coefficient of variation (CV)0.0461969779
Kurtosis-1.260885038
Mean1383667.07
Median Absolute Deviation (MAD)58111
Skewness0.03135403636
Sum1569078457
Variance4085924544
MonotonicityNot monotonic
2022-01-30T02:27:53.504913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13748034
 
0.4%
14582124
 
0.4%
12868264
 
0.4%
14354274
 
0.4%
14516794
 
0.4%
13027884
 
0.4%
13682064
 
0.4%
14399534
 
0.4%
12897293
 
0.3%
13198033
 
0.3%
Other values (908)1096
96.6%
ValueCountFrequency (%)
12727371
0.1%
12728671
0.1%
12728961
0.1%
12732581
0.1%
12739861
0.1%
12741121
0.1%
12744832
0.2%
12746371
0.1%
12747911
0.1%
12748271
0.1%
ValueCountFrequency (%)
14914241
 
0.1%
14907443
0.3%
14901192
0.2%
14897152
0.2%
14894442
0.2%
14893272
0.2%
14889772
0.2%
14889291
 
0.1%
14888971
 
0.1%
14888522
0.2%

customer_id
Real number (ℝ≥0)

Distinct767
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175122.3748
Minimum242
Maximum404649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:53.664241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum242
5-th percentile44368.95
Q180795
median133202
Q3276192
95-th percentile377650.4
Maximum404649
Range404407
Interquartile range (IQR)195397

Descriptive statistics

Standard deviation114493.5564
Coefficient of variation (CV)0.6537917074
Kurtosis-1.034138897
Mean175122.3748
Median Absolute Deviation (MAD)68808
Skewness0.6310068611
Sum198588773
Variance1.310877446 × 1010
MonotonicityNot monotonic
2022-01-30T02:27:53.811833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
528328
 
0.7%
2761927
 
0.6%
2705257
 
0.6%
970796
 
0.5%
472805
 
0.4%
2417195
 
0.4%
1517895
 
0.4%
3472634
 
0.4%
820414
 
0.4%
1864734
 
0.4%
Other values (757)1079
95.1%
ValueCountFrequency (%)
2421
0.1%
51391
0.1%
54441
0.1%
56931
0.1%
79221
0.1%
96661
0.1%
148691
0.1%
174951
0.1%
189021
0.1%
224051
0.1%
ValueCountFrequency (%)
4046491
0.1%
4030191
0.1%
4022151
0.1%
4020601
0.1%
4009501
0.1%
4007662
0.2%
3997291
0.1%
3995841
0.1%
3991271
0.1%
3989301
0.1%

courier_id
Real number (ℝ≥0)

Distinct321
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96332.69136
Minimum3296
Maximum177847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:53.972862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3296
5-th percentile18349.7
Q148473.25
median104533
Q3140684
95-th percentile164384
Maximum177847
Range174551
Interquartile range (IQR)92210.75

Descriptive statistics

Standard deviation49682.37544
Coefficient of variation (CV)0.5157374381
Kurtosis-1.250080908
Mean96332.69136
Median Absolute Deviation (MAD)43011.5
Skewness-0.2213600537
Sum109241272
Variance2468338430
MonotonicityNot monotonic
2022-01-30T02:27:54.114398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9921932
 
2.8%
10453319
 
1.7%
6190018
 
1.6%
3258018
 
1.6%
15655718
 
1.6%
329616
 
1.4%
2250215
 
1.3%
3973315
 
1.3%
12358114
 
1.2%
3074314
 
1.2%
Other values (311)955
84.2%
ValueCountFrequency (%)
329616
1.4%
35921
 
0.1%
59351
 
0.1%
67151
 
0.1%
687310
0.9%
783311
1.0%
86251
 
0.1%
90781
 
0.1%
94761
 
0.1%
95494
 
0.4%
ValueCountFrequency (%)
1778472
0.2%
1759121
 
0.1%
1755552
0.2%
1754853
0.3%
1741863
0.3%
1734093
0.3%
1727001
 
0.1%
1726861
 
0.1%
1723992
0.2%
1723012
0.2%

vehicle_type
Categorical

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
bicycle
849 
car
217 
walker
 
36
truck
 
10
van
 
10
Other values (2)
 
12

Length

Max length10
Median length7
Mean length6.157848325
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbicycle
2nd rowbicycle
3rd rowbicycle
4th rowbicycle
5th rowbicycle

Common Values

ValueCountFrequency (%)
bicycle849
74.9%
car217
 
19.1%
walker36
 
3.2%
truck10
 
0.9%
van10
 
0.9%
scooter9
 
0.8%
motorcycle3
 
0.3%

Length

2022-01-30T02:27:54.268208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-30T02:27:54.342175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
bicycle849
74.9%
car217
 
19.1%
walker36
 
3.2%
truck10
 
0.9%
van10
 
0.9%
scooter9
 
0.8%
motorcycle3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pickup_place
Categorical

HIGH CARDINALITY

Distinct189
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
The Meatball Shop
96 
Blue Ribbon Sushi
 
66
sweetgreen
 
55
RedFarm Broadway
 
44
Rubirosa
 
28
Other values (184)
845 

Length

Max length33
Median length14
Mean length14.59259259
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)5.6%

Sample

1st rowBlue Ribbon Sushi
2nd rowFriedman's Lunch
3rd rowThe Grey Dog - University
4th rowBlue Ribbon Sushi Bar & Grill
5th rowilili Restaurant

Common Values

ValueCountFrequency (%)
The Meatball Shop96
 
8.5%
Blue Ribbon Sushi66
 
5.8%
sweetgreen55
 
4.9%
RedFarm Broadway44
 
3.9%
Rubirosa28
 
2.5%
TAO28
 
2.5%
RedFarm Hudson26
 
2.3%
Parm25
 
2.2%
Han Dynasty25
 
2.2%
Sushi of Gari 4623
 
2.0%
Other values (179)718
63.3%

Length

2022-01-30T02:27:54.476910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sushi164
 
5.9%
the137
 
4.9%
blue109
 
3.9%
ribbon107
 
3.9%
shop99
 
3.6%
meatball96
 
3.5%
redfarm70
 
2.5%
68
 
2.5%
sweetgreen55
 
2.0%
bar47
 
1.7%
Other values (299)1819
65.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

place_category
Categorical

HIGH CORRELATION

Distinct36
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Italian
188 
Japanese
181 
Chinese
116 
Sushi
91 
American
89 
Other values (31)
469 

Length

Max length16
Median length7
Mean length7.220458554
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowJapanese
2nd rowBreakfast
3rd rowCoffee
4th rowJapanese
5th rowMiddle Eastern

Common Values

ValueCountFrequency (%)
Italian188
16.6%
Japanese181
16.0%
Chinese116
10.2%
Sushi91
 
8.0%
American89
 
7.8%
Salad56
 
4.9%
Burger46
 
4.1%
Seafood35
 
3.1%
Indian33
 
2.9%
Mexican33
 
2.9%
Other values (26)266
23.5%

Length

2022-01-30T02:27:54.613281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italian188
15.8%
japanese181
15.2%
chinese116
 
9.7%
american104
 
8.7%
sushi91
 
7.6%
salad56
 
4.7%
burger46
 
3.9%
seafood35
 
2.9%
eastern33
 
2.8%
mexican33
 
2.8%
Other values (29)309
25.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

item_name
Categorical

HIGH CARDINALITY

Distinct774
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Chicken
 
18
Edamame
 
10
Spicy Tuna & Tempura Flakes
 
9
Cheeseburger
 
9
Classic Beef
 
9
Other values (769)
1079 

Length

Max length79
Median length15
Mean length16.77160494
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique609 ?
Unique (%)53.7%

Sample

1st rowSpicy Tuna & Tempura Flakes
2nd rowSweet Potato Fries
3rd rowThree Eggs Any Style
4th rowUnagi
5th rowHommus

Common Values

ValueCountFrequency (%)
Chicken18
 
1.6%
Edamame10
 
0.9%
Spicy Tuna & Tempura Flakes9
 
0.8%
Cheeseburger9
 
0.8%
Classic Beef9
 
0.8%
Sake8
 
0.7%
Miso8
 
0.7%
Barest Burger8
 
0.7%
Kale Caesar8
 
0.7%
House Salad8
 
0.7%
Other values (764)1039
91.6%

Length

2022-01-30T02:27:54.870883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicken102
 
3.4%
79
 
2.6%
salad66
 
2.2%
spicy66
 
2.2%
roll56
 
1.8%
tuna37
 
1.2%
rice35
 
1.2%
with32
 
1.1%
pork32
 
1.1%
dumplings32
 
1.1%
Other values (898)2494
82.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

item_quantity
Real number (ℝ≥0)

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.16313933
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:54.993412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5321269284
Coefficient of variation (CV)0.4574919915
Kurtosis23.2637379
Mean1.16313933
Median Absolute Deviation (MAD)0
Skewness4.325086616
Sum1319
Variance0.2831590679
MonotonicityNot monotonic
2022-01-30T02:27:55.087551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
11005
88.6%
293
 
8.2%
321
 
1.9%
412
 
1.1%
62
 
0.2%
51
 
0.1%
ValueCountFrequency (%)
11005
88.6%
293
 
8.2%
321
 
1.9%
412
 
1.1%
51
 
0.1%
62
 
0.2%
ValueCountFrequency (%)
62
 
0.2%
51
 
0.1%
412
 
1.1%
321
 
1.9%
293
 
8.2%
11005
88.6%

item_category_name
Categorical

HIGH CARDINALITY

Distinct321
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
Appetizers
 
58
Salads
 
36
Signatures
 
35
Naked Balls
 
33
Dim Sum
 
31
Other values (316)
941 

Length

Max length53
Median length10
Mean length11.67283951
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)15.7%

Sample

1st rowMaki (Special Rolls)
2nd rowMarket Sides
3rd rowBreakfast
4th rowTaiseyo (Atlantic Ocean)
5th rowDips

Common Values

ValueCountFrequency (%)
Appetizers58
 
5.1%
Salads36
 
3.2%
Signatures35
 
3.1%
Naked Balls33
 
2.9%
Dim Sum31
 
2.7%
Sandwiches28
 
2.5%
Maki (Special Rolls)24
 
2.1%
Sides22
 
1.9%
Entrees21
 
1.9%
Rolls20
 
1.8%
Other values (311)826
72.8%

Length

2022-01-30T02:27:55.213943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
91
 
4.4%
appetizers82
 
3.9%
rolls79
 
3.8%
salads72
 
3.4%
sushi54
 
2.6%
dinner52
 
2.5%
sides41
 
2.0%
special37
 
1.8%
sandwiches36
 
1.7%
signatures35
 
1.7%
Other values (322)1510
72.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

how_long_it_took_to_order
Categorical

HIGH CARDINALITY
UNIFORM

Distinct862
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
06:36.3
 
5
07:34.7
 
4
10:48.8
 
4
21:41.4
 
4
06:02.0
 
4
Other values (857)
1113 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique655 ?
Unique (%)57.8%

Sample

1st row03:45.0
2nd row09:19.6
3rd row07:40.6
4th row10:21.7
5th row05:17.0

Common Values

ValueCountFrequency (%)
06:36.35
 
0.4%
07:34.74
 
0.4%
10:48.84
 
0.4%
21:41.44
 
0.4%
06:02.04
 
0.4%
02:42.04
 
0.4%
08:04.44
 
0.4%
06:19.94
 
0.4%
08:04.84
 
0.4%
03:03.84
 
0.4%
Other values (852)1093
96.4%

Length

2022-01-30T02:27:55.338724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06:36.35
 
0.4%
08:04.84
 
0.4%
15:18.04
 
0.4%
05:17.84
 
0.4%
14:36.94
 
0.4%
04:34.34
 
0.4%
11:37.04
 
0.4%
03:03.84
 
0.4%
07:34.74
 
0.4%
06:19.94
 
0.4%
Other values (852)1093
96.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pickup_lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct213
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.74112718
Minimum40.67176012
Maximum40.8180821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:55.462328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40.67176012
5-th percentile40.71811007
Q140.72362962
median40.735725
Q340.75893876
95-th percentile40.7821167
Maximum40.8180821
Range0.14632198
Interquartile range (IQR)0.035309145

Descriptive statistics

Standard deviation0.02226077121
Coefficient of variation (CV)0.0005463955652
Kurtosis0.4822409652
Mean40.74112718
Median Absolute Deviation (MAD)0.01297043
Skewness0.4955914944
Sum46200.43822
Variance0.0004955419349
MonotonicityNot monotonic
2022-01-30T02:27:55.614562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7261096666
 
5.8%
40.782116744
 
3.9%
40.7215451236
 
3.2%
40.7450785933
 
2.9%
40.7227485928
 
2.5%
40.7628577628
 
2.5%
40.7342138526
 
2.3%
40.7230198325
 
2.2%
40.73221325
 
2.2%
40.7840772623
 
2.0%
Other values (203)800
70.5%
ValueCountFrequency (%)
40.671760122
0.2%
40.673478062
0.2%
40.674250953
0.3%
40.677683122
0.2%
40.679289371
 
0.1%
40.679694211
 
0.1%
40.68673311
 
0.1%
40.687497241
 
0.1%
40.6876894
0.4%
40.688539111
 
0.1%
ValueCountFrequency (%)
40.81808216
 
0.5%
40.802549614
 
0.4%
40.796066661
 
0.1%
40.790399761
 
0.1%
40.787613113
 
0.3%
40.785160046
 
0.5%
40.784877344
 
0.4%
40.7840772623
2.0%
40.783987341
 
0.1%
40.782116744
3.9%

pickup_lon
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct212
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.98871594
Minimum-74.01544645
Maximum-73.95023268
Zeros0
Zeros (%)0.0%
Negative1134
Negative (%)100.0%
Memory size9.0 KiB
2022-01-30T02:27:55.763579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-74.01544645
5-th percentile-74.0085433
Q1-74.000286
median-73.98882151
Q3-73.9807391
95-th percentile-73.95754527
Maximum-73.95023268
Range0.06521377
Interquartile range (IQR)0.0195469

Descriptive statistics

Standard deviation0.01390595026
Coefficient of variation (CV)-0.0001879469062
Kurtosis0.1086269355
Mean-73.98871594
Median Absolute Deviation (MAD)0.00933504
Skewness0.6974039548
Sum-83903.20388
Variance0.0001933754526
MonotonicityNot monotonic
2022-01-30T02:27:55.920774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.0024918666
 
5.8%
-73.980739144
 
3.9%
-73.9888424336
 
3.2%
-73.9888215133
 
2.9%
-73.97161928
 
2.5%
-73.9960619228
 
2.5%
-74.0062020326
 
2.3%
-73.98807225
 
2.2%
-73.99585425
 
2.2%
-73.9776224123
 
2.0%
Other values (202)800
70.5%
ValueCountFrequency (%)
-74.015446451
 
0.1%
-74.010557463
 
0.3%
-74.010104141
 
0.1%
-74.0097320122
1.9%
-74.0087498812
1.1%
-74.008692517
1.5%
-74.00854333
 
0.3%
-74.0082506517
1.5%
-74.008122682
 
0.2%
-74.0075111410
0.9%
ValueCountFrequency (%)
-73.950232684
 
0.4%
-73.951656047
 
0.6%
-73.952786836
 
0.5%
-73.95447444
 
0.4%
-73.955182195
 
0.4%
-73.955240251
 
0.1%
-73.955926614
 
0.4%
-73.9563341518
1.6%
-73.957545279
0.8%
-73.959221472
 
0.2%

dropoff_lat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct737
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.74346596
Minimum40.65214519
Maximum40.848324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:56.092075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40.65214519
5-th percentile40.71021966
Q140.72534655
median40.7405728
Q340.76166155
95-th percentile40.78353229
Maximum40.848324
Range0.19617881
Interquartile range (IQR)0.036315

Descriptive statistics

Standard deviation0.02412616962
Coefficient of variation (CV)0.00059214819
Kurtosis0.4244660661
Mean40.74346596
Median Absolute Deviation (MAD)0.0159939
Skewness0.2891951503
Sum46203.09039
Variance0.0005820720606
MonotonicityNot monotonic
2022-01-30T02:27:56.238777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.73242828
 
0.7%
40.7237298
 
0.7%
40.7274057
 
0.6%
40.7391927
 
0.6%
40.72332956
 
0.5%
40.7780286
 
0.5%
40.764602736
 
0.5%
40.75207646
 
0.5%
40.72606416
 
0.5%
40.75062135
 
0.4%
Other values (727)1069
94.3%
ValueCountFrequency (%)
40.652145192
0.2%
40.666888361
0.1%
40.6705841
0.1%
40.6747621
0.1%
40.67696791
0.1%
40.6781611
0.1%
40.6842221
0.1%
40.6847562
0.2%
40.68530891
0.1%
40.6862811
0.1%
ValueCountFrequency (%)
40.8483241
 
0.1%
40.80902131
 
0.1%
40.80892922
0.2%
40.8081643
0.3%
40.80735561
 
0.1%
40.806469791
 
0.1%
40.8064651
 
0.1%
40.805662
0.2%
40.80491222
0.2%
40.80465351
 
0.1%

dropoff_lon
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct737
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.98668972
Minimum-74.0176786
Maximum-73.93488
Zeros0
Zeros (%)0.0%
Negative1134
Negative (%)100.0%
Memory size9.0 KiB
2022-01-30T02:27:56.395497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-74.0176786
5-th percentile-74.00961075
Q1-74.001549
median-73.9902554
Q3-73.975954
95-th percentile-73.95387245
Maximum-73.93488
Range0.0827986
Interquartile range (IQR)0.025595

Descriptive statistics

Standard deviation0.01784718385
Coefficient of variation (CV)-0.0002412215483
Kurtosis-0.4802902637
Mean-73.98668972
Median Absolute Deviation (MAD)0.011989035
Skewness0.6145893871
Sum-83900.90614
Variance0.0003185219714
MonotonicityNot monotonic
2022-01-30T02:27:56.547049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.00407618
 
0.7%
-74.00805948
 
0.7%
-73.9918027
 
0.6%
-73.9873267
 
0.6%
-74.00101566
 
0.5%
-73.9538516
 
0.5%
-73.980104816
 
0.5%
-73.99150186
 
0.5%
-73.98977936
 
0.5%
-73.99025545
 
0.4%
Other values (727)1069
94.3%
ValueCountFrequency (%)
-74.01767862
0.2%
-74.0169432
0.2%
-74.01688791
 
0.1%
-74.01586653
0.3%
-74.0156171
 
0.1%
-74.01557531
 
0.1%
-74.01514111
 
0.1%
-74.01482341
 
0.1%
-74.01423041
 
0.1%
-74.0141922
0.2%
ValueCountFrequency (%)
-73.934884
0.4%
-73.9406351
 
0.1%
-73.94103773
0.3%
-73.9415211
 
0.1%
-73.94215581
 
0.1%
-73.9428732
0.2%
-73.9435221
 
0.1%
-73.94438962
0.2%
-73.9456212
0.2%
-73.945676942
0.2%

when_the_delivery_started
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size9.0 KiB

when_the_courier_arrived_at_pickup
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size9.0 KiB

when_the_courier_left_pickup
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size9.0 KiB

when_the_courier_arrived_at_dropoff
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size9.0 KiB

distance_crossed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct867
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.709489926
Minimum0.02828587842
Maximum12.02197417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2022-01-30T02:27:56.698528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02828587842
5-th percentile0.3581607419
Q10.8335324382
median1.361151338
Q32.16849021
95-th percentile4.291154938
Maximum12.02197417
Range11.99368829
Interquartile range (IQR)1.334957772

Descriptive statistics

Standard deviation1.326627512
Coefficient of variation (CV)0.7760370457
Kurtosis7.422955246
Mean1.709489926
Median Absolute Deviation (MAD)0.6166009038
Skewness2.205710774
Sum1938.561576
Variance1.759940554
MonotonicityNot monotonic
2022-01-30T02:27:56.950058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65154546685
 
0.4%
1.369317764
 
0.4%
0.30546933144
 
0.4%
1.7009705874
 
0.4%
4.7005922154
 
0.4%
1.4073507734
 
0.4%
1.8917826644
 
0.4%
2.168490214
 
0.4%
1.7168540234
 
0.4%
1.3154860314
 
0.4%
Other values (857)1093
96.4%
ValueCountFrequency (%)
0.028285878423
0.3%
0.12217766031
 
0.1%
0.15425758591
 
0.1%
0.16950976331
 
0.1%
0.17724466982
0.2%
0.17742627751
 
0.1%
0.18708270221
 
0.1%
0.20768939371
 
0.1%
0.22019765652
0.2%
0.23345280532
0.2%
ValueCountFrequency (%)
12.021974171
 
0.1%
8.8679129121
 
0.1%
8.2900870921
 
0.1%
7.4713509411
 
0.1%
7.449280564
0.4%
7.2159113321
 
0.1%
7.1722001791
 
0.1%
7.1581112271
 
0.1%
6.9999866862
0.2%
6.7259838261
 
0.1%

Interactions

2022-01-30T02:27:50.753210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:37.163400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:38.541270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:40.227266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:41.744938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:43.296320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:44.648655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:46.151392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:47.717510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:49.291854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:50.896279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:37.301385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:38.677819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:40.396316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:41.888362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:43.418480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:44.791927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:46.392820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:47.864073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:49.522808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:51.030095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:37.433044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:38.809736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:40.537522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:42.026461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:43.547258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:44.922837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:46.528601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:48.070423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:49.641142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:51.165069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:37.582319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:39.001687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:40.693367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:42.178102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:43.696747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:45.074705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:46.681709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:48.219525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:49.783386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:51.318056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:37.720890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:39.192301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:40.846216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:42.326437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:43.814667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:45.217854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:46.837450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:48.371624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:49.920946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:51.453961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:37.847626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:39.347092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:40.990022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:42.462597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:43.968831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:45.383349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:46.976614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:48.515664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:50.052595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:51.601568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:37.981264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:39.511011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:41.143391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:42.612185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:44.110860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:45.542034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:47.127581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:48.670578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:50.194442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:51.744135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:38.120134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:39.649559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:41.291655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:42.757257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:44.245872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:45.698420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:47.277433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:48.830247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:50.334908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:51.898276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:38.272932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:39.793968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:41.452901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:42.911240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:44.390923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:45.863062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:47.432514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:48.994509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:50.484915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:52.034136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:38.407840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:39.928866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:41.597993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:43.040717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:44.519222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:46.006366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:47.574036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:49.137814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-30T02:27:50.620473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-30T02:27:57.082439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-30T02:27:57.282572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-30T02:27:57.471080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-30T02:27:57.650314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-30T02:27:57.791276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-30T02:27:52.358766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-30T02:27:52.788011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexdelivery_idcustomer_idcourier_idvehicle_typepickup_placeplace_categoryitem_nameitem_quantityitem_category_namehow_long_it_took_to_orderpickup_latpickup_londropoff_latdropoff_lonwhen_the_delivery_startedwhen_the_courier_arrived_at_pickupwhen_the_courier_left_pickupwhen_the_courier_arrived_at_dropoffdistance_crossed
041327707122609118095bicycleBlue Ribbon SushiJapaneseSpicy Tuna & Tempura Flakes2.0Maki (Special Rolls)03:45.040.726110-74.00249240.709323-74.01586700:07:18.50000000:14:42.70000000:25:19.40000000:48:27.2000002.179898
12614598713915437833bicycleFriedman's LunchBreakfastSweet Potato Fries1.0Market Sides09:19.640.742681-74.00623640.740126-74.00468700:14:29.80000000:30:04.30000000:41:53.90000000:47:320.312452
22714196748159196818bicycleThe Grey Dog - UniversityCoffeeThree Eggs Any Style1.0Breakfast07:40.640.733787-73.99303540.744274-73.98045900:05:5500:18:07.10000000:34:28.20000000:50:48.9000001.576253
32912822729799172941bicycleBlue Ribbon Sushi Bar & GrillJapaneseUnagi1.0Taiseyo (Atlantic Ocean)10:21.740.767653-73.98292540.748828-73.99013900:02:4100:11:48.20000000:28:00.70000000:45:08.7000002.177474
4321467996301380142394bicycleilili RestaurantMiddle EasternHommus1.0Dips05:17.040.744188-73.98753140.734994-73.98770900:18:37.40000000:30:46.40000000:46:07.10000000:57:18.1000001.021089
5351314550348787119813bicycleOtto Enoteca PizzeriaItalianLentils ���Toscana�۝1.0Vegetables22:42.940.732064-73.99615540.767582-73.98370400:40:38.80000000:49:5000:27:58.50000000:55:56.8000004.081993
646137918947440112646bicycleBareburgerBurgerBarest Burger1.0Bareburgers06:25.140.728478-73.99839240.715170-74.01160100:54:08.30000000:57:53.90000000:08:40.40000000:23:14.7000001.851868
750132213349104156557carEmpanada Mama (closed)MexicanReggaeton1.0Wheat Flour05:35.040.764217-73.98832140.747897-74.00360800:10:10.30000000:21:06.70000000:33:26.10000000:42:17.5000002.225111
85714013046811667103truckBlue Ribbon Sushi IzakayaJapaneseRock Shrimp & Crispy Garlic Teriyaki1.0Kushi Yaki (Skewers)08:13.940.721980-73.98814840.729999-73.99368800:30:30.50000000:47:48.30000000:01:10.30000000:22:12.8000001.006065
967132512941991108647bicycleBlue Ribbon Sushi Bar & GrillJapaneseTamago1.0Taiheiyo (Pacific Ocean)12:20.840.767653-73.98292540.763269-73.96311300:01:43.80000000:23:34.10000000:38:53.50000000:49:00.3000001.742220

Last rows

df_indexdelivery_idcustomer_idcourier_idvehicle_typepickup_placeplace_categoryitem_nameitem_quantityitem_category_namehow_long_it_took_to_orderpickup_latpickup_londropoff_latdropoff_lonwhen_the_delivery_startedwhen_the_courier_arrived_at_pickupwhen_the_courier_left_pickupwhen_the_courier_arrived_at_dropoffdistance_crossed
11245948144123011158152502bicycleCaracas Arepa BarSouth AmericanDe Pabellon1.0Arepas03:59.040.726810-73.98532940.734876-73.98665800:07:11.50000000:14:31.40000000:35:21.50000000:43:09.2000000.902734
112559491369210117810156557carMomoyaSushiCrispy Rice1.0Signature Roll05:14.240.784077-73.97762240.784312-73.95193400:05:37.80000000:14:53.70000000:26:14.50000000:37:21.1000002.168490
112659501386459331143104533bicycleWaverly DinerAmericanChicken Fingers2.0Appetizers & Finger Foods06:38.140.733012-74.00005640.732486-73.99658500:14:29.80000000:36:13.10000000:52:07.80000000:55:43.3000000.298980
11275952134787940500130599bicycleJ. G. MelonBurgerBacon Cheeseburger1.0Main Menu05:32.240.771135-73.95962340.766204-73.96075700:08:42.80000000:19:13.60000000:34:33.70000000:42:57.3000000.555946
11285955135009699288120179carSushi of Gari 46SushiEdamame1.0Appetizers24:25.140.760654-73.98958340.716209-74.01557500:19:48.90000000:59:28.20000000:37:32.50000000:57:22.9000005.401863
1129595713625249516432580bicycleAmmaIndianNaan3.0Breads05:50.940.755702-73.96858740.779801-73.98765000:10:23.10000000:27:14.50000000:19:05.20000000:36:27.4000003.122970
113059581294906221206113300bicycleEmpanada Mama (closed)MexicanPizza1.0Wheat Flour07:35.740.764217-73.98832140.779967-73.98333500:29:10.10000000:30:32.50000000:05:52.60000000:18:40.4000001.798971
11315973137995312710369993bicycleThe Grey Dog - UniversityCoffeeOrganic Raw Veggies1.0Small Plates04:34.440.733787-73.99303540.736760-73.98204800:35:3000:43:14.40000000:50:04.60000000:57:28.4000000.985082
113259751475459303211156557carBig Nick's Burgers & PizzaPizzaChicken Fingers1.0Appetizers & Side Orders05:11.140.776767-73.97912040.793834-73.94152100:47:25.90000000:48:56.20000000:11:46.10000000:25:02.1000003.696529
113359821357449128517134189carRedFarm BroadwayChinesePan-Fried Pork Buns (4)1.0Dim Sum09:25.340.782117-73.98073940.732714-73.99504000:30:44.50000000:40:22.50000000:06:02.10000000:31:54.4000005.617469